
from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
					from .deepspeed import (
					    DeepSpeedEngineWrapper,
					    DeepSpeedOptimizerWrapper,
					    DeepSpeedSchedulerWrapper,
					    DummyOptim,
					    DummyScheduler,
					    HfDeepSpeedConfig,
					)

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers





'''simple docstring'''

import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__     :						int	=  get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')

@require_sentencepiece
@require_tokenizers
class 							a__( snake_case__   ,      unittest.TestCase     ):
			a_	: List[Any]									=      GPTSwaTokenizer
			a_	: int									=      False
			a_	: List[Any]									=      True
			a_	: Any									=      False






			def 						_lowercase  (     self						)							->							str:
					super().setUp()

					# We have a SentencePiece fixture for testing
					snake_case__	     =GPTSwaTokenizer(_UpperCAmelCase					,   eos_token='<unk>'					,   bos_token='<unk>'					,   pad_token='<unk>'						)

					tokenizer.save_pretrained(self.tmpdirname						)






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Any:
					snake_case__	     ='This is a test'
					snake_case__	     ='This is a test'
					return input_text, output_text






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     ='<s>'
					snake_case__	     =1

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase						)					,   _UpperCAmelCase						)
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase						)					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =list(self.get_tokenizer().get_vocab().keys()						)

					self.assertEqual(vocab_keys[0]					,   '<unk>'						)
					self.assertEqual(vocab_keys[1]					,   '<s>'						)
					self.assertEqual(vocab_keys[-1]					,   'j'						)
					self.assertEqual(len(_UpperCAmelCase						)					,   2000						)






			def 						_lowercase  (     self						)							->							List[str]:
					self.assertEqual(self.get_tokenizer().vocab_size					,   2000						)






			def 						_lowercase  (     self						)							->							Any:
					snake_case__	     =GPTSwaTokenizer(_UpperCAmelCase						)

					snake_case__	     =tokenizer.tokenize('This is a test'						)
					self.assertListEqual(_UpperCAmelCase					,   ['▁This', '▁is', '▁a', '▁t', 'est']						)

					self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)					,   [465, 287, 265, 631, 842]						)

					snake_case__	     =tokenizer.tokenize('I was born in 92000, and this is falsé.'						)
					# fmt: off
					self.assertListEqual(
					    _UpperCAmelCase					,   ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']					,   )
					# fmt: on

					snake_case__	     =tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)
					self.assertListEqual(
					    _UpperCAmelCase					,   [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]					,   )

					snake_case__	     =tokenizer.convert_ids_to_tokens(_UpperCAmelCase						)
					# fmt: off
					self.assertListEqual(
					    _UpperCAmelCase					,   ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']						)
					# fmt: on






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =GPTSwaTokenizer(_UpperCAmelCase						)
					snake_case__	     =['This is a test', 'I was born in 92000, and this is falsé.']
					snake_case__	     =[
					    [465, 287, 265, 631, 842],
					    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
					]

					# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
					for text, expected_ids in zip(_UpperCAmelCase					,   _UpperCAmelCase						):
							self.assertListEqual(tokenizer.encode_fast(_UpperCAmelCase						)					,   _UpperCAmelCase						)

					# Test that decode_fast returns the input text
					for text, token_ids in zip(_UpperCAmelCase					,   _UpperCAmelCase						):
							self.assertEqual(tokenizer.decode_fast(_UpperCAmelCase						)					,   _UpperCAmelCase						)






			@slow
			def 						_lowercase  (     self						)							->							Dict:
					snake_case__	     =[
					    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
					    'Hey there, how are you doing this fine day?',
					    'This is a text with a trailing spaces followed by a dot     .',
					    'Häj sväjs lillebrör! =)',
					    'Det är inget fel på Mr. Cool',
					]

					# fmt: off
					snake_case__	     ={'input_ids': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
					# fmt: on
					self.tokenizer_integration_test_util(
					    expected_encoding=_UpperCAmelCase					,   model_name='AI-Sweden/gpt-sw3-126m'					,   sequences=_UpperCAmelCase					,   )




